Automatic video object extraction

ABSTRACT

Automatic video object extraction that defines substantially precise objects is disclosed. In one embodiment, color segmentation and motion segmentation are performed on a source video. The color segmentation segments the video by substantially uniform color regions thereof. The motion segmentation segments the video by moving regions thereof. The color regions and the moving regions are then combined to define the video objects. In varying embodiments, pre-processing and post-processing is performed to further clean the source video and the video objects defined, respectively.

FIELD OF THE INVENTION

[0001] This invention relates generally to the extraction of objectsfrom source video, and more particularly to such extraction that issubstantially automatic in nature.

BACKGROUND OF THE INVENTION

[0002] An increasingly common use of computers and computerized devicesis the processing of video, such as video captured in real-time, orvideo captured or otherwise input from a storage, such as a hard diskdrive, a digital video disc (DVD), a video cassette recorder (VCR) tape,etc. For the processing of video, objects within the video usually needto be extracted. Objects can correspond to, for example, semanticobjects, which are objects as defined perceptually by the viewer. Forexample, a video of a baseball game may have as its objects the variousplayers on the field, the baseball after it is thrown or hit, etc.Object extraction is useful for object-based coding techniques, such asMPEG-4, as known within the art; for content-based visual database queryand indexing applications, such as MPEG-7, as also known within the art;for the processing of objects in video sequences; etc.

[0003] Prior art object extraction techniques generally fall into one oftwo categories: automatic extraction and semi-automatic extraction.Automatic extraction is relatively easy for the end user to perform,since he or she needs to provide little or no input for the objects tobe extracted. Automatic extraction is also useful in real-timeprocessing of video, where user input cannot be feasibly provided inreal time. The primary disadvantage to automatic extraction, however, isthat as performed within the prior art the objects are not definedprecisely. That is, only rough contours of objects are identified. Forexample, parts of the background may be included in the definition of agiven object.

[0004] Conversely, semi-automatic object extraction from video requiresuser input. Such user input can provide the exact contours of objects,for example, so that the objects are defined more precisely as comparedto prior art automatic object extraction. The disadvantage tosemi-automatic extraction, however, is that user input is in factnecessary. For the lay user, this may be at best inconvenient, and atmost infeasible in the case where the user is not proficient in videoapplications and does not know how to provide the necessary optimalinput. Furthermore, semi-automatic extraction is ill-suited forreal-time processing of video, even where a user is proficient, sincetypically the user cannot identify objects in real time.

[0005] Therefore, there is a need to combine the advantages of automaticand semi-automatic video object extraction techniques. That is, there isa need to combine the advantageous precise definitions afforded objectsby semi-automatic techniques, with the advantageous ability to performthe object extraction in real-time, as is allowed with automatictechniques. For these and other reasons, there is a need for the presentinvention.

SUMMARY OF THE INVENTION

[0006] The invention relates to automatic video object extraction. Inone embodiment, color segmentation and motion segmentation are performedon a source video. The color segmentation segments the video bysubstantially uniform color regions thereof. The motion segmentationsegments the video by moving regions thereof. The color regions and themoving regions, referred to as masks in one embodiment of the invention,are then combined to define the video objects.

[0007] Embodiments of the invention provide for advantages not foundwithin the prior art. Specifically, at least some embodiments of theinvention provide for object extraction from video in a substantiallyautomatic manner, while resulting in objects that are substantiallyprecisely defined. The motion segmentation mask defines the basiccontours of the objects, while the color segmentation mask provides formore precise boundaries of these basic contours. Thus, combined, themotion and color segmentation masks allow for video object extractionthat is substantially automatic, but which still yields substantiallyprecisely defined objects.

[0008] The invention includes computer-implemented methods,machine-readable media, computerized systems, and computers of varyingscopes. Other aspects, embodiments and advantages of the invention,beyond those described here, will become apparent by reading thedetailed description and with reference to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009]FIG. 1 is a diagram of an operating environment in conjunctionwith which embodiments of the invention can be practiced;

[0010]FIG. 2 is a diagram of a frame of a representative source video inconjunction with which embodiments of the invention can be practiced;

[0011]FIG. 3 is a diagram of a first object extracted from therepresentative source video of FIG. 2;

[0012]FIG. 4 is a diagram of a second object extracted from therepresentative source video of FIG. 2;

[0013]FIG. 5 is a flowchart of a method to perform color segmentationaccording to one embodiment of the invention;

[0014]FIG. 6 is a flowchart of a method according to one embodiment ofthe invention; and,

[0015]FIG. 7 is a diagram of a system according to one embodiment of theinvention.

DETAILED DESCRIPTION OF THE INVENTION

[0016] In the following detailed description of exemplary embodiments ofthe invention, reference is made to the accompanying drawings which forma part hereof, and in which is shown by way of illustration specificexemplary embodiments in which the invention may be practiced. Theseembodiments are described in sufficient detail to enable those skilledin the art to practice the invention, and it is to be understood thatother embodiments may be utilized and that logical, mechanical,electrical and other changes may be made without departing from thespirit or scope of the present invention. The following detaileddescription is, therefore, not to be taken in a limiting sense, and thescope of the present invention is defined only by the appended claims.

[0017] Some portions of the detailed descriptions which follow arepresented in terms of algorithms and symbolic representations ofoperations on data bits within a computer memory. These algorithmicdescriptions and representations are the means used by those skilled inthe data processing arts to most effectively convey the substance oftheir work to others skilled in the art. Am algorithm is here, andgenerally, conceived to be a self-consistent sequence of steps leadingto a desired result. The steps are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated.

[0018] It has proven convenient at times, principally for reasons ofcommon usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like. It should be borne inmind, however, that all of these and similar terms are to be associatedwith the appropriate physical quantities and are merely convenientlabels applied to these quantities. Unless specifically stated otherwiseas apparent from the following discussions, it is appreciated thatthroughout the present invention, discussions utilizing terms such asprocessing or computing or calculating or determining or displaying orthe like, refer to the action and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission or display devices.

[0019] Operating Environment

[0020] Referring to FIG. 1, a diagram of the hardware and operatingenvironment in conjunction with which embodiments of the invention maybe practiced is shown. The description of FIG. 1 is intended to providea brief, general description of suitable computer hardware and asuitable computing environment in conjunction with which the inventionmay be implemented. Although not required, the invention is described inthe general context of computer-executable instructions, such as programmodules, being executed by a computer, such as a personal computer.Generally, program modules include routines, programs, objects,components, data structures, etc., that perform particular tasks orimplement particular abstract data types.

[0021] Moreover, those skilled in the art will appreciate that theinvention may be practiced with other computer system configurations,including hand-held devices, multiprocessor systems,microprocessor-based or programmable consumer electronics, network PC's,minicomputers, mainframe computers, ASICs (Application SpecificIntegrated Circuits), and the like. The invention may also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules may belocated in both local and remote memory storage devices.

[0022] The exemplary hardware and operating environment of FIG. 1 forimplementing the invention includes a general purpose computing devicein the form of a computer 20, including a processing unit 21, a systemmemory 22, and a system bus 23 that operatively couples various systemcomponents include the system memory to the processing unit 21. Theremay be only one or there may be more than one processing unit 21, suchthat the processor of computer 20 comprises a single central-processingunit (CPU), or a plurality of processing units, commonly referred to asa parallel processing environment. The computer 20 may be a conventionalcomputer, a distributed computer, or any other type of computer; theinvention is not so limited.

[0023] The system bus 23 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. The system memorymay also be referred to as simply the memory, and includes read onlymemory (ROM) 24 and random access memory (RAM) 25. A basic input/outputsystem (BIOS) 26, containing the basic routines that help to transferinformation between elements within the computer 20, such as duringstart-up, is stored in ROM 24. The computer 20 further includes a harddisk drive 27 for reading from and writing to a hard disk, not shown, amagnetic disk drive 28 for reading from or writing to a removablemagnetic disk 29, and an optical disk drive 30 for reading from orwriting to a removable optical disk 31 such as a CD ROM or other opticalmedia.

[0024] The hard disk drive 27, magnetic disk drive 28, and optical diskdrive 30 are connected to the system bus 23 by a hard disk driveinterface 32, a magnetic disk drive interface 33, and an optical diskdrive interface 34, respectively. The drives and their associatedcomputer-readable media provide nonvolatile storage of computer-readableinstructions, data structures, program modules and other data for thecomputer 20. It should be appreciated by those skilled in the art thatany type of computer-readable media which can store data that isaccessible by a computer, such as magnetic cassettes, flash memorycards, digital video disks, Bernoulli cartridges, random access memories(RAMs), read only memories (ROMs), and the like, may be used in theexemplary operating environment.

[0025] A number of program modules may be stored on the hard disk,magnetic disk 29, optical disk 31, ROM 24, or RAM 25, including anoperating system 35, one or more application programs 36, other programmodules 37, and program data 38. A user may enter commands andinformation into the personal computer 20 through input devices such asa keyboard 40 and pointing device 42. Other input devices (not shown)may include a microphone, joystick, game pad, satellite dish, scanner,video camera, or the like. These and other input devices are oftenconnected to the processing unit 21 through a serial port interface 46that is coupled to the system bus, but may be connected by otherinterfaces, such as a parallel port, game port, an IEEE 1394 port (alsoknown as FireWire), or a universal serial bus (USB). A monitor 47 orother type of display device is also connected to the system bus 23 viaan interface, such as a video adapter 48. In addition to the monitor,computers typically include other peripheral output devices (not shown),such as speakers and printers.

[0026] The computer 20 may operate in a networked environment usinglogical connections to one or more remote computers, such as remotecomputer 49. These logical connections are achieved by a communicationdevice coupled to or a part of the computer 20; the invention is notlimited to a particular type of communications device. The remotecomputer 49 may be another computer, a server, a router, a network PC, aclient, a peer device or other common network node, and typicallyincludes many or all of the elements described above relative to thecomputer 20, although only a memory storage device 50 has beenillustrated in FIG. 1. The logical connections depicted in FIG. 1include a local-area network (LAN) 51 and a wide-area network (WAN) 52.Such networking environments are commonplace in office networks,enterprise-wide computer networks, intranets and the Internet, which areall types of networks.

[0027] When used in a LAN-networking environment, the computer 20 isconnected to the local network 51 through a network interface or adapter53, which is one type of communications device. When used in aWAN-networking environment, the computer 20 typically includes a modem54, a type of communications device, or any other type of communicationsdevice for establishing communications over the wide area network 52,such as the Internet. The modem 54, which may be internal or external,is connected to the system bus 23 via the serial port interface 46. In anetworked environment, program modules depicted relative to the personalcomputer 20, or portions thereof, may be stored in the remote memorystorage device. It is appreciated that the network connections shown areexemplary and other means of and communications devices for establishinga communications link between the computers may be used.

[0028] Overview

[0029] In this section of the detailed description, an overview ofobject extraction, such as can be performed by embodiments of theinvention, is provided. The objects extracted in at least someembodiments of the invention are semantic objects. Semantic objectsrepresent meaningful entities in a source video, from a perceptualstandpoint of users.

[0030] For example, in the diagram of FIG. 2, within the source video200 are shown two objects, a person 200, and a bird 204. Applying videoobject extraction in accordance with embodiments of the invention yieldsseparation of the person 200 and the bird 204 from the source video 200in such a manner that the boundaries of the objects are defined at leastsubstantially precisely, against any other objects within the video 200and the background of the video 200.

[0031] Thus, applying embodiments of the invention to perform objectextraction on the source video 200 of FIG. 2 yields the person 202 byitself, as shown in the frame 300 of the diagram of FIG. 3, and the bird204 by itself, as shown in the frame 400 of the diagram of FIG. 4. Thoseof ordinary skill within the art can appreciate, however, that theoverview presented in this section in conjunction with FIGS. 2-4 is forexample and illustration purposes only, and that the invention itself isnot limited to the example provided herein.

[0032] Color Segmentation

[0033] In this section of the detailed description, color segmentationas performed in accordance with an embodiment of the invention isdescribed. The color segmentation in one embodiment is performed on aframe of a source video, to segment the video by substantially uniformcolor regions thereof. That is, a color segmentation mask is generatedthat defines substantially precise boundaries of the objects extractedfrom the source video. Those of ordinary skill within the art canappreciate that many techniques exist within the prior art to performcolor segmentation, such as edge detection, region techniques, Maximum Aposteriori Probability (MAP) techniques, etc., and that the inventionitself is not limited to a particular technique. The embodiment of theinvention described herein utilizes a technique that extends and isbased on the technique described in the reference Chuang Gu andMing-Chieh Lee, Tracking of Multiple Semantic Video Objects for InternetApplications, SPIE, Visual Communications and Image Processing 1999,volume 3653, as known within the art.

[0034] Referring to FIG. 5, a flowchart of a method to perform colorsegmentation according to an embodiment of the invention, extending andbased on the reference identified in the previous paragraph, is shown.From the start in 500, the method proceeds to 502, where the methoddetermines a seed pixel of a frame of a source video. Initially, a seedcan be chosen to be the upper-left corner pixel in a rectangular frame.After that, a seed is randomly chosen from the region(s) have not beenincluded in any segmented regions. If no seed pixels are left, then themethod proceeds to 514. Otherwise the method puts the seed into a seedbuffer in 504. A seed pixel is then obtained from the buffer in 506,unless there are no seeds left in the buffer, in which case the methodproceeds back to 502.

[0035] In 508, the seed pixel is grown by a neighbor pixel or aneighborhood of pixels. The seed pixel is grown by a neighborhood ofpixels surrounding the pixel as governed by the constraint that asubstantially homogenous color region is to be generated. In oneembodiment, the homogeneity of a region is controlled by the differenceof the maximum and minimum values within a region. For example, for acolor image, the value of a pixel in one embodiment is a vectorincluding the red, green and blue color channels in the form {r, g, b}.Thus, the maximum and minimum values of a region are {max {r}, max{g},max{b}}, and {min{r}, min{g}, min{b}}, respectively. If the differenceof the maximum and minimum values of a region does not exceed apredetermined threshold, then it is deemed a uniform region.

[0036] Therefore, in 510, after the region has been grown in 508, it isdetermined whether the region is still of substantially uniform color.If not, then the method proceeds back to 506, to obtain a new seed fromthe buffer, and to start the process of obtaining substantially uniformcolor regions over again. Otherwise, the neighboring pixel or pixels ofthe seed pixel is placed into the seed buffer in 512, to continuegrowing the same substantially uniform color region by also proceedingback to 506. The same substantially uniform color region is continued tobe grown by virtue of the fact that the neighboring pixel or pixels ofthe seed pixel have been placed into the seed buffer in 512.

[0037] Once all the substantially uniform color regions have beendetermined, and there are no seeds left in the buffer, nor can furtherseeds be found within the frame of the source video, then the methodproceeds from 502 to 514. In 514, smaller substantially uniform colorregions are merged into larger substantially uniform color regions. Thismerging removes relatively smaller regions by integrating them intorelatively larger regions. In one embodiment, this is accomplished bymerging all the regions with the number of pixels less than a certainthreshold (<10 pixels) to its neighbor regions. The method then ends in516.

[0038] As has been noted, color segmentation can be performed on one ormore frames of the source video. The invention is not limited, however,by the manner which is followed to select the frame that will ultimatelybe used as the color segmentation mask. In one embodiment, the frame isselected by the user, either before or after color segmentation has beenperformed (i.e., the user selects a frame on which color segmentation isperformed, or color segmentation is performed on a number of frames, andthe user selects one of these frames). In another embodiment, the frameis predetermined. For example, for off-line processing of capturedvideo, the first frame may be always selected. As another example, forreal-time processing of video, the last frame may always be selected.

[0039] Motion Segmentation and Combination of Multiple Frames

[0040] In this section of the detailed description, motion segmentationas performed in accordance with an embodiment of the invention isdescribed, as well as the combination of multiple frames for such motionsegmentation to generate a cleaner motion segmentation mask. The motionsegmentation in one embodiment is performed on a plurality of frames ofa source video, such as two frames, as will be described, although theinvention itself is not so limited. Those of ordinary skill within theart can appreciate that many techniques exist within the prior art toperform motion segmentation, such as optical flow techniques,block-based matching techniques, Maximum A posteriori Probability (MAP)techniques, simultaneous motion estimation and segmentation techniques,etc., and that the invention itself is not limited to a particulartechnique. The motion segmentation segments a source video by movingregions thereof, and defines approximate boundaries of objects extractedfrom the source video.

[0041] In one embodiment, motion segmentation is obtained as follows.First, a motion vector is first obtained by region matching. For eachuniform region generated from the color segmentation described in thepreceding paragraph, prior to combining the smaller regions with thelarger regions, a motion vector is obtained by determining the bestmatch in a next frame of the source video. This is particularlydescribed in the reference noted in the preceding section of thedetailed description. This region-based motion estimation techniquesubstantially ensures that each color-segmented region has the samemotion vector.

[0042] In one embodiment, the matching window is set to asixteen-by-sixteen pixel window, and the matching criterion is todetermine the least matching error of the region. The matching error isdefined as:${{ERROR}\left( {n,i} \right)} = {\sum\limits_{p \in R_{n}}{{{I_{t}(p)} - {I_{t + 1}\left( {p + V_{({n,i})}} \right)}}}}$

[0043] ERROR(n, i) is the matching error for region n with motion vectorV_(n, i). I_t and I_t+1 represent the current and next frame,respectively. R_n denotes region n. Operator ∥*∥ denotes the sum ofabsolute difference between two vectors. Finally, V_(n, i)<=V_max, whereV_max is the searching range. The motion vector of region n is definedas:${V(n)} = {\underset{V_{({n,i})}}{\arg \quad \min}\quad {{ERROR}\left( {n,i} \right)}}$

[0044] After obtaining a motion vector for each region, a motion maskcan then be obtained: V(i, j) = V(n), (i, j) ∈ Region  nV(i, j) = V_(x)(i, j) + V_(y)(i, j)${M\left( {i,j} \right)} = \left\{ \begin{matrix}1 & {{{if}\quad {{V\left( {i,j} \right)}}} \geq {T1}} \\0 & {otherwise}\end{matrix} \right.$

[0045] V(i, j) is the motion vector of pixel (i, j), and V_x(i,j) andV_y(i,j) are the projection of V(i, j) on the x and y coordinates,respectively. M(i, j) represents each pixel in the motion mask, where T1is the predetermined threshold set according to the motion. In oneembodiment, T1 is set to one for slow motion, and to anywhere from twoto three for fast motion.

[0046] In one embodiment, the frame interval is adapted according to theestimated motion. Most motion estimation is based on two continuousframes. However, this is not always valid in some sequences, where themotion of moving objects is minimal, and leads to a sparse motion field.Therefore, the interval of two frames is adapted. For a fast motionfield, the interval can be set small, while for slow motion, theinterval can be set large.

[0047] In one embodiment, multiple frames are combined to decreaseerrors associated with the assignment of regions incorrectly assigned toa moving object due to noise. In other words, the incorrect motioncaused by random noise is removed by checking multiple motion masks togenerate the final motion mask, since random noise will not be constantacross multiple frames. Furthermore, the uncovered background can bedistinguished from true moving regions.

[0048] This combination of multiple frames is accomplished in oneembodiment as follows. First, the frequency of a pixel is assigned to amoving object in a number of motion masks, such as ten or more. If thefrequency is higher than a predetermined threshold, then this pixel isdetermined as a moving pixel. Otherwise, it is designated as backgroundand is removed from the final motion mask. In one embodiment, thethreshold is fifty percent. Thus,${C\left( {i,j} \right)} = {\sum\limits_{s = 1}^{S}{M_{s}\left( {i,j} \right)}}$${M\left( {i,j} \right)} = \left\{ \begin{matrix}1 & {{{if}\quad {{C\left( {i,j} \right)}/S}} > {T2}} \\0 & {otherwise}\end{matrix} \right.$

[0049] M_s(i,j) represents each motion mask being combined, S denotesthe number of total motion masks used in combination, C(i,j) denotes thetotal number of times pixel (i,j) is assigned as a moving pixel in Smotion masks, and T2 is the threshold.

[0050] Methods

[0051] In this section of the detailed description, methods according tovarying embodiments of the invention are described. The description ismade with reference to FIG. 6, which is a flowchart of acomputer-implemented method according to one embodiment of theinvention. The computer-implemented method is desirably realized atleast in part as one or more programs running on a computer—that is, asa program executed from a computer-readable medium such as a memory by aprocessor of a computer. The programs are desirably storable on amachine-readable medium such as a floppy disk or a CD-ROM, fordistribution and installation and execution on another computer.

[0052] Referring to FIG. 6, from the start of the method in 600, themethod proceeds to 602, in which in one embodiment pre-processing of asource video from which objects are to be extracted is accomplished.Pre-processing is performed in one embodiment to remove noise from thesource video prior to performing color segmentation. In one embodiment,a median filter is used on each of the red, green and blue colorchannels of the source video, or one or more frames thereof.Pre-processing is not necessary for the invention, however.

[0053] Next, in 604, color segmentation is performed. Color segmentationcan be accomplished in one embodiment as described in a precedingsection of the detailed description, although the invention is not solimited. The resulting mask of the color segmentation is then optimizedin one embodiment by merging smaller regions into larger regions in 606,and such that a certain frame of the color segmented source video isselected in 608, as has also been described in a preceding section ofthe detailed description. The final color segmentation mask is then usedas input in 616.

[0054] In the embodiment of FIG. 6, the color segmentation maskresulting from 604 is used as the basis for motion segmentation in 610,to generate a motion segmentation mask, as described in the precedingsection of the detailed description. However, the invention does notrequire the motion segmentation mask to be generated from the colorsegmentation mask. In one embodiment, the mask is optimized by combiningmultiple-frame masks in 612, as also described in the preceding sectionof the detailed description. However, the invention is not limited tomotion segmentation as described in the preceding section. The finalmotion segmentation mask is then used as input in 616.

[0055] In 614, a third mask used as input in 616 is generated, which isreferred to as a frame difference mask. In one embodiment, the framedifference mask is generated from two successive frames of the sourcevideo, and is generated to provide for correction of errors that mayresult from the motion segmentation mask. The difference mask isobtained in one embodiment as follows:D(i, j) = I_(t)(i, j) − I_(t + m)(i, j)${{DM}\left( {i,j} \right)} = \left\{ \begin{matrix}1 & {{{if}\quad {D\left( {i,j} \right)}} > {T4}} \\0 & {otherwise}\end{matrix} \right.$

[0056] The difference mask is DM(i, j), where I_x represents frame x,and T4 is a predetermined threshold, such as ten to twenty pixels.

[0057] In 616, the resulting color segmentation, motion segmentation,and frame difference masks are combined to define the objects of thesource video in an automatic and substantially precise manner. In oneembodiment, the median result of color segmentation and motionsegmentation is first determined. The frame difference mask is thencombined with this median result to generate the final mask whichdefines the objects of the source video.

[0058] Thus, while color segmentation identifies the exact edges ofobjects, it typically results in over segmentation. Furthermore, whilemotion segmentation generates a coarse mask of moving objects, theboundaries identified are too rough to provide for exact objectextraction. Therefore, the color segmentation and motion segmentationmasks are combined, to extract moving objects with substantiallypixel-wise accuracy.

[0059] In one embodiment, this initial combination of motion and colorsegmentation masks is accomplished by a mapping operation. For eachregion generated by color segmentation, the corresponding region isfound in the motion segmentation mask. If the percent of the region thatis assigned to a moving object exceeds a predetermined threshold, thenthe whole region is deemed to be part of the moving object. In oneembodiment, this threshold is between fifty and sixty percent.

[0060] Thus,${B(N)} = {\sum\limits_{{({i,j})} \in \quad N}{M\left( {i,j} \right)}}$${J(N)} = \left\{ {{\begin{matrix}1 & {{{if}\quad {{B(N)}/{A(N)}}} > {T3}} \\0 & {otherwise}\end{matrix}F\quad {M\left( {i,j} \right)}} = {{{J(N)}\quad {if}\quad \left( {i,j} \right)} \in N}} \right.$

[0061] N represents the color segmented region, A(N) is the area ofregion N, M(i, j) is the pixel in the combined motion mask, and FM(I, j)is the pixel in the final mapped mask. T3 is the threshold.

[0062] The combined motion and color segmentation mask is then combinedwith the frame difference mask, as follows:${B(N)} = {\sum\limits_{{({i,j})} \in \quad N}{{DM}\left( {i,j} \right)}}$${J(N)} = \left\{ {{\begin{matrix}1 & {{{if}\quad {{B(N)}/{A(N)}}} > {T3}} \\0 & {otherwise}\end{matrix}{{FD}\left( {i,j} \right)}} = {{{{J(N)}\quad {if}\quad \left( {i,j} \right)} \in {N{F\left( {i,j} \right)}}} = \left\{ \begin{matrix}1 & {{{if}\quad F\quad {M\left( {i,j} \right)}} = {{1\quad {and}\quad {{FD}\left( {i,j} \right)}} = 1}} \\0 & {otherwise}\end{matrix} \right.}} \right.$

[0063] F(i, j) is the final mask in which the color segmentation, motionsegmentation, and frame-difference masks have been combined. FD(i, j) isthe frame-difference mask such that the mask has been mapped in the samemanner as FM(i, j) has been mapped, so that the two can be compared togenerate F(i, j).

[0064] As can be appreciated by those of ordinary skill within the art,the frame-difference mask, while providing for more precise automaticextraction of video objects, is not required in one embodiment of theinvention. Thus, in one embodiment, F(i, j) can be obtained simply bysetting it equal to FM(i, j), which is the interim result of combiningthe color and the motion segmentation masks. That is, combining themoving regions resulting from the motion segmentation and thesubstantially uniform color regions resulting from the colorsegmentation can be used in one embodiment to define the objects of thesource video.

[0065] Furthermore, other masks can be introduced into 616 in additionto and/or in lieu of any of the color segmentation, motion segmentation,and frame difference masks. Such masks can include a depth mask,providing for information relating to the depth of the objects of thesource video, a texture mask, providing for information relating to thetexture of the objects of the source video, etc. In such embodiments,all of the masks to be used are combined to generate the final mask thatidentifies the objects extracted from the source video.

[0066] Next, in one embodiment, post-processing of the mask generated in616 is performed in 618, for example, to remove noise from the mask.Post-processing can include in one embodiment removing small holes fromthe objects identified to generate integrated objects, and can inanother embodiment also include removing small regions from backgroundareas of the source video to generate cleaner objects. In oneembodiment, this is accomplished by first merging smaller regions withlarger regions, as described in a preceding section of the detaileddescription in the context of color segmentation. Then, a morphologicaloperator(s), such as open and/or close, as known within the art, isapplied. However, as can be appreciated by those of ordinary skillwithin the art, post-processing is not required by the invention itself.The method of FIG. 6 finally ends at 620.

[0067] Systems and Computers

[0068] In this section of the detailed description, systems andcomputers according to varying embodiments of the invention aredescribed. The description is made with reference to FIG. 7, which is adiagram of a computer 699 according to an embodiment of the invention.The computer 699 can, for example, correspond to the computer describedin conjunction with FIG. 1 in a preceding section of the detaileddescription. The computer 699 includes a processor 700, acomputer-readable medium 702, and a source 704 from which the sourcevideo from which objects are to be extracted is obtained. The medium 702can include non-volatile memory, storage devices such as hard diskdrives, as well as volatile memory such as types of random-access memory(RAM).

[0069] The medium 702 stores data representing at least one frame 706 ofthe source video, which itself has a number of frames, and is obtainedfrom the source 704, such as a video camera, a video cassette recorder(VCR) tape, a digital video disc (DVD), etc. The medium 702 also storesdata representing the objects 708 extracted from the source video. Themedium 702 can in one embodiment correspond to a means for storing datarepresenting the frame(s) 706 and the objects 708. The objects areextracted by a computer program 710, which is executed by the processor700 from the medium 702.

[0070] The program 710 is thus designed to extract the objects 708 fromthe video by generating and then combining a number of masks from thevideo, in one embodiment, as has been described in preceding sections ofthe detailed description. In one embodiment, the program 710 cancorrespond to a means for generating and combining the masks. Thesemasks can include a color segmentation mask, a motion segmentation mask,a frame difference mask, a texture mask, and/or a depth mask, as hasbeen described in the preceding section of the detailed description. Thecolor mask can be used to define substantially precise boundaries of theobjects, the motion segmentation mask to define approximate boundaries,and the frame difference mask to correct errors within the motionsegmentation mask.

[0071] Furthermore, the program 710 can either pre-process one or moreframes of the source video to remove noise, post-process the objectsextracted in the finally generated mask to remove noise, or both,although the invention is not so limited. Pre-processing andpost-processing can be performed as has been described in the precedingsection of the detailed description. In one embodiment, the program 710also corresponds to the means for pre-processing and/or post-processing.

[0072] Conclusion

[0073] Although specific embodiments have been illustrated and describedherein, it will be appreciated by those of ordinary skill in the artthat any arrangement which is calculated to achieve the same purpose maybe substituted for the specific embodiments shown. This application isintended to cover any adaptations or variations of the presentinvention. Therefore, it is manifestly intended that this invention belimited only by the following claims and equivalents thereof.

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 30. Acomputer implemented method for extracting objects from a source videocomprising: performing motion segmentation on a plurality of frames ofthe source video to segment the source video by moving regions thereofto define basic contours of the objects of the source video; performingcolor segmentation on a frame of the source video to segment the sourcevideo by substantially uniform color regions thereof to define moreprecise boundaries of the objects of the source video as measuredagainst the basic contours of the objects of the source video as definedby the motion segmentation; and, combining at least the moving regionsresulting from the motion segmentation and the substantially uniformcolor regions resulting from the color segmentation to define still moreprecisely the objects of the source video by, at least partially,deeming a substantially uniform color region to be part of a movingobject if a percent of the substantially uniform color region that isassignable to one or more moving regions exceeds a predeterminedthreshold.
 31. The method of claim 30, wherein the predeterminedthreshold is between fifty and sixty percent.
 32. A machine readablemedium having instructions stored thereon for execution by a processorto perform a method comprising: generating a plurality of masks from asource video having a plurality of frames, the plurality of masksincluding at least a color segmentation mask, a motion segmentationmask, and a frame difference mask; wherein the frame difference mask isgenerated from at least two frames of the plurality of frames withoutusing another mask; and, combining the plurality of masks to define aplurality of objects of the source video; wherein generating theplurality of masks comprises generating at least the color segmentationmask to define substantially precise boundaries of the objects, themotion segmentation mask to define approximate boundaries of theobjects, and the frame difference mask to correct errors within themotion segmentation mask; and wherein the plurality of objects of thesource video resulting from the combining are more precise than thesubstantially precise boundaries of the objects defined by the colorsegmentation mask, which are more precise than the approximateboundaries of the objects defined by the motion segmentation mask. 33.The medium of claim 32, wherein generating the color segmentation maskcomprises growing substantially uniform color regions of a frame of theplurality of frames of the source video.
 34. The medium of claim 32,wherein generating the motion segmentation mask comprises generating themotion segmentation mask from the plurality of frames of the sourcevideo.
 35. The medium of claim 32, wherein the method further comprisespre processing a frame of the plurality of frames of the source video toremove noise prior to generating the plurality of masks.
 36. The mediumof claim 32, wherein the method further comprises post processing theplurality of objects of the source video to remove noise from theobjects.
 37. A computer comprising: a processor; at least one computerreadable medium to store data representing: at least two frames of aplurality of frames of a source video, and, a plurality of objectsextracted from the source video; and, a computer program executed by theprocessor from the at least one computer readable medium and designed toextract the plurality of objects from the source video by generating aplurality of masks from the source video and then combining theplurality of masks, the plurality of masks including: a motionsegmentation mask that defines basic contours of the plurality ofobjects of the source video based on moving regions thereof, a colorsegmentation mask that defines more precise boundaries, as compared tothe basic contours, of the plurality of objects of the source videobased on substantially uniform color regions thereof, and a framedifference mask that is generated from the at least two frames of theplurality of frames of the source video without using feedback.
 38. Thecomputer of claim 37, wherein the plurality of objects extracted fromthe source video are better defined than the precise boundaries of theplurality of objects as defined by the color segmentation mask, and theprecise boundaries of the plurality of objects are more defined than thebasic contours of the plurality of objects as, defined by the motionsegmentation mask.
 39. The computer of claim 37, wherein the program isfurther designed to pre process the at least two frames of the pluralityof frames of the source video to remove noise.
 40. The computer of claim37, wherein the program is further designed to post process theplurality of objects extracted from the source video to remove noise.41. At least one machine readable medium having instructions storedthereon for execution by a processor to transform a general purposecomputer to a special purpose computer comprising: means for storingdata representing: at least two frames of a plurality of frames of asource video, and, a plurality of objects extracted from the sourcevideo; and, means for: (a) generating a plurality of masks from thesource video, the plurality of masks including: a color segmentationmask segmenting the source video by substantially uniform color regionsto define substantially precise boundaries of the plurality of objects,a motion segmentation mask segmenting the source video by moving regionsthereof to define approximate boundaries of the plurality of objects,and a frame difference mask to reflect differences between the at leasttwo frames of the source video, and, (b) combining the plurality ofmasks to extract the plurality of objects from the source video by, atleast partially, deeming a substantially uniform color region to be partof a moving object if a percent of the substantially uniform colorregion that is assignable to one or more moving regions exceeds apredetermined threshold.
 42. The at least one machine readable medium ofclaim 41, wherein the substantially precise boundaries of the pluralityof objects defined by the color segmentation mask are more precise thanthe approximate boundaries of the plurality of objects defined by themotion segmentation mask, and the plurality of objects extracted fromthe source video are more precise than the precise boundaries of theplurality of objects defined by the color segmentation mask.
 43. The atleast one machine readable medium of claim 41, wherein the means isfurther for pre processing the at least two frames of the plurality offrames of the source video to remove noise.
 44. The at least one machinereadable medium of claim 41, wherein the means is further for postprocessing the plurality of objects extracted from the source video toremove noise.
 45. At least one machine readable medium havinginstructions stored thereon for execution by a processor to perform amethod comprising: performing motion segmentation on at least threeframes of a plurality of frames of video to segment the video by movingregions and to thereby define basic contours of objects of the video;performing color segmentation on at least one frame of the plurality offrames of the video to segment the video by substantially uniform colorregions and to thereby define more precise boundaries of the objects ofthe video as compared to the basic contours of the objects of the videoas defined by the motion segmentation; and, combining the substantiallyuniform color regions resulting from the color segmentation and themoving regions resulting from the motion segmentation to further definethe objects of the video by, at least partially, deeming a substantiallyuniform color region to be part of a moving object if a percent of thesubstantially uniform color region that is assignable to one or moremoving regions exceeds a predetermined threshold.
 46. The at least onemachine readable medium of claim 45, wherein the performing motionsegmentation comprises determining a combination motion mask based on aplurality of individual motion masks and responsive to at least onethreshold.
 47. At least one machine readable medium having instructionsstored thereon for execution by a processor to perform a methodcomprising: generating a color segmentation mask that defines contoursof objects of a video to a first precision based on colors of the video;generating a motion segmentation mask that defines the contours of theobjects of the video to a second precision based on motions of thevideo; generating a frame difference mask that reflects differences inthe video between a first frame and a second frame of the video on a perpixel basis responsive to a predetermined threshold; and combining thecolor segmentation mask, the motion segmentation mask, and the framedifference mask to define the objects of the video.
 48. The at least onemachine readable medium of claim 47, wherein the generating the motionsegmentation mask comprises segmenting the video by moving regionsthereof to define basic contours of the objects of the video; whereinthe generating the color segmentation mask comprises segmenting thevideo by substantially uniform color regions to define more preciseboundaries of the objects of the video as compared to the basic contoursof the objects of the video; and wherein the objects of the videodefined by the combining of the color segmentation mask, the motionsegmentation mask, and the frame difference mask are yet more precisethan the basic contours of the objects of the video defined by themoving regions and the more precise boundaries of the objects of thevideo defined by the substantially uniform color regions.
 49. The atleast one machine readable medium of claim 47, wherein the combining ofthe color segmentation mask, the motion segmentation mask, and the framedifference mask to define the objects of the video comprises: combiningthe color segmentation mask and the motion segmentation mask to generatea first intermediate mask; combining the color segmentation mask and theframe difference mask to generate a second intermediate mask; andcombining the first intermediate mask and the second intermediate maskto produce a final mask.